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[实验室人员] [CV][PhD Candidate]Chunhe Song

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发表于 2009-5-14 21:11 | 只看该作者 | 倒序看帖 | 打印
PH.D. Candidate Chunhe Song
BSc THU; MS NEU

Tel: (+86) 024-83681067-10
Fax: (+86) 024-62123002-118
Email: neujw@126.com
Chunhe Song was born in Anshan, Liaoning province, China, in 1981. He received the B.S. degree from the Dep. of Mechanical Engineering, THU, Beijing, China, in 2003. From 2003 to 2005, he served as a R&D engineer in American-Standard Corporation, and he received the M.S. degree from the Dep. of Information Science and Technology, NEU, Shenyang, China, in 2008.  He is currently working toward the PH.D. degree at the same University. He has published a series of paper mainly topics on neural network, nonlinear filtering and optimization algorithm, and now he also serves as a reviewer of some international conferences.




Research Interests
Neural Network, Nonlinear Filtering, Optimization Algorithm and Dynamic Bayesian Network
Current Works
Genetic Regulatory Networks with DBN and NN, detail information will be shown later

Selected Publications
Chunhe Song, Hai Zhao, et al, The Limited Mutation Particle Swarm Optimizer, Lecture Notes in Computer Science, LSMS2007, LNCS4688, pp.258-266, 2007.
Chunhe Song, HaiZhao, et al, The Distance-guided Particle Swarm Optimizer with Dynamic Mutation, IIHMSP-2007, vol.2, pp.342-345, 2007.
HaiZhao, Chunhe Song, et al. Design and Implementation of Turbine Governor Model in Hydropower Simulation Software System, Journal of Northeastern University (Natural Science), 2008(01): 37-40, 2008.
HaiZhao, Chunhe Song, et al. Study on Higher Dimensional Object Function of PSO algorithm, Journal of Northeastern University (Natural Science), 2008(05): 649-652, 2008.
HaiZhao, Chunhe Song, et al. Application of Self-tuning-fuzzy PID Algorithm in Hydraulic Turbine Governor, Journal of Northeastern University (Natural Science), 2007(12): 1701-1704, 2008.
Acknowledgement
Special thankfulness to my parents Renbo Song and Shuju Pang, my tutor professor Hai Zhao and my beautiful young lady Miss Wei Jing  for the long-time supports.
有句老话叫历史总是惊人的相似,不过今天已被改为历史总是雷人地相似。

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